code: def norm_to_lognorm_variance(mu: Tensor, var: Tensor) -> Tensor:
      """Compute variance of a log-MVN from its MVN marginals
  
      Args:
          mu: A `batch_shape x n` mean vector of the Normal distribution.
          var: A `batch_shape x n` variance vectorof the Normal distribution.
  
      Returns:
          The `batch_shape x n` variance vector of the log-Normal distribution.
      """
      b = mu + 0.5 * var
      return (torch.exp(var) - 1) * torch.exp(2 * b)
  
file_path: botorch/botorch/models/transforms/utils.py
imports: from __future__ import annotations
  from functools import wraps
  from typing import Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: norm_to_lognorm_variance(mu, var) and returns: unknown